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On the Importance of Text Analysis for Stock Price Prediction
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014)
Abstract
We investigate the importance of text analysis for stock price prediction. In particular, we introduce a system that forecasts companies stock price changes (UP, DOWN, STAY) in response to financial events reported in 8-K documents. Our results indicate that using text boosts prediction accuracy over 10% (relative) over a strong baseline that incorporates many financially-rooted features. This impact is most important in the short term (i.e., the next day after the financial event) but persists for up to five days.